{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a4d051f",
   "metadata": {},
   "source": [
    "### 读取提取模板所保存的光谱数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f8ada368",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "df1 = pd.read_csv(rf'E:\\code\\Spectrum_analyse\\SpectralAnalysis\\tobacoo_0830_上午.csv', index_col= 0 ,  header=0)\n",
    "df2 = pd.read_csv(rf'E:\\code\\Spectrum_analyse\\SpectralAnalysis\\tobacoo_0830_下午.csv', index_col= 0 ,  header=0)\n",
    "# df3 = pd.read_csv(rf'E:\\code\\Spectrum_analyse\\SpectralAnalysis\\tobacoo_8030下午_C4F.csv', index_col= 0 ,  header=0 )\n",
    "# df4 = pd.read_csv(rf'E:\\code\\Spectrum_analyse\\SpectralAnalysis\\tobacoo_8030下午_C3F_2.csv', index_col= 0 ,  header=0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f157ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# values_concatenated = np.concatenate((df1.values, df2.values, df3.values , df4.values), axis=0)\n",
    "values_concatenated = np.concatenate((df1.values, df2.values), axis=0)\n",
    "df = pd.DataFrame(values_concatenated)\n",
    "df.columns = df1.columns\n",
    "df.index = list(df1.index) + list(df2.index)\n",
    "# df.index = list(df1.index) + list(df2.index) + list(df3.index)+ list(df4.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d824de68",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "labels = df.index.values\n",
    "labels =  [i.split('-')[1] for i in labels]\n",
    "wave = [float(i) for i in df.columns[0:19]]\n",
    "spectrum = df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b705988",
   "metadata": {},
   "source": [
    "### 绘图查看数据情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eed65a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import importlib\n",
    "import SpectrumAnalyse  # 导入模块\n",
    "importlib.reload(SpectrumAnalyse)\n",
    "# 导入需要的函数\n",
    "from SpectrumAnalyse import plot_duplicate_data_both, split_data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "category,  split_datas = split_data(labels , spectrum[:,0:19])\n",
    "plot_duplicate_data_both(category,  split_datas , wave  , save=False) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83597805",
   "metadata": {},
   "source": [
    "### 预处理方法列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8b059fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "# def none(data):\n",
    "#     scaler = StandardScaler()\n",
    "#     data = scaler.fit_transform(data.copy())\n",
    "#     return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef310b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm  # 导入 tqdm 库\n",
    "importlib.reload(SpectrumAnalyse)\n",
    "from SpectrumAnalyse import msc, snv, CT, MA, SG, pca ,none\n",
    "from SpectrumAnalyse import SPA_select, k_fold_cross_validation, leave_one_out_cross_validation, mean_squared_error, pearson_correlation, plot_duplicate_data, plot_duplicate_data_both, plot_selected_features, split_data\n",
    "\n",
    "preprocessing_functions = {\n",
    "    '仅做标准化': none,\n",
    "    'MSC': msc,\n",
    "    'SNV': snv,\n",
    "    'CT': CT,\n",
    "    # 'MA': MA,\n",
    "    # 'SG': SG,\n",
    "    # 'PCA': pca,\n",
    "\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d2ffde",
   "metadata": {},
   "source": [
    "### 网格调参结合各种预处理方法选优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a4bef69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Functions:   0%|          | 0/4 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Functions:  25%|██▌       | 1/4 [00:54<02:42, 54.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Functions:  50%|█████     | 2/4 [01:24<01:20, 40.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Functions:  75%|███████▌  | 3/4 [01:37<00:27, 27.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Functions: 100%|██████████| 4/4 [02:30<00:00, 37.64s/it]\n"
     ]
    },
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
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         "name": "0",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "1",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "2",
         "rawType": "float64",
         "type": "float"
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       ],
       "ref": "8ac0e92f-af9d-46f8-ac86-23d1d0b8444f",
       "rows": [
        [
         "0",
         "仅做标准化",
         "{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}",
         "0.86"
        ],
        [
         "1",
         "MSC",
         "{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}",
         "0.7"
        ],
        [
         "2",
         "SNV",
         "{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}",
         "0.69"
        ],
        [
         "3",
         "CT",
         "{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}",
         "0.86"
        ]
       ],
       "shape": {
        "columns": 3,
        "rows": 4
       }
      },
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>仅做标准化</td>\n",
       "      <td>{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSC</td>\n",
       "      <td>{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SNV</td>\n",
       "      <td>{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CT</td>\n",
       "      <td>{'C': 500, 'gamma': 'scale', 'kernel': 'linear'}</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0                                                 1     2\n",
       "0  仅做标准化  {'C': 500, 'gamma': 'scale', 'kernel': 'linear'}  0.86\n",
       "1    MSC  {'C': 500, 'gamma': 'scale', 'kernel': 'linear'}  0.70\n",
       "2    SNV  {'C': 500, 'gamma': 'scale', 'kernel': 'linear'}  0.69\n",
       "3     CT  {'C': 500, 'gamma': 'scale', 'kernel': 'linear'}  0.86"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy as np\n",
    "import random\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 创建 StandardScaler 对象\n",
    "scaler = StandardScaler()\n",
    "\n",
    "\n",
    "\n",
    "random_seed = 53\n",
    "random.seed(random_seed)\n",
    "np.random.seed(random_seed)\n",
    "\n",
    "# 定义SVM模型\n",
    "model = SVC()\n",
    "\n",
    "# 定义参数网格\n",
    "param_grid = {\n",
    "    'C': [1,  100 ,500],  # 惩罚参数\n",
    "    'kernel': ['linear'],  # 核函数类型\n",
    "    'gamma': ['scale']  # 核函数的gamma值\n",
    "}\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "# 使用 StratifiedKFold 并传递 random_state\n",
    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_seed)\n",
    "# 创建GridSearchCV对象，使用5折交叉验证（可以调整交叉验证的折数）\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=cv, n_jobs=1, verbose=1, scoring='accuracy' )\n",
    "\n",
    "test_results = []\n",
    "\n",
    "# 使用 tqdm 进行进度条显示\n",
    "for name, func in tqdm(preprocessing_functions.items(), desc=\"Processing Functions\"):\n",
    "\n",
    "    results = []\n",
    "    results.append(name)\n",
    "\n",
    "    # 预处理流水线，可自行添加\n",
    "    processed_spectrum = func(spectrum.copy())\n",
    "    processed_spectrum = scaler.fit_transform(processed_spectrum)\n",
    "\n",
    "    # 使用K-折交叉验证网格调参\n",
    "    grid_search.fit(processed_spectrum, labels)\n",
    "\n",
    "    results.append(grid_search.best_params_)\n",
    "\n",
    "    best_model = grid_search.best_estimator_\n",
    "    best_score = k_fold_cross_validation(processed_spectrum, np.array(labels), best_model, n_splits=5 , random_state = random_seed , show= False)\n",
    "\n",
    "    results.append(np.array(best_score).round(2))\n",
    "    test_results.append(results)\n",
    "\n",
    "pd.DataFrame(test_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96aac280",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy as np\n",
    "import random\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "random_seed = 53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f1192f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8564885496183207"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model =SVC(C=1000 , kernel='linear' , gamma='scale')\n",
    "spectrum_scaled = scaler.fit_transform(spectrum.copy())\n",
    "k_fold_cross_validation(snv(spectrum_scaled), np.array(labels), best_model, n_splits=5 , random_state = random_seed )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "565f4b5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8564885496183207"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "k_fold_cross_validation2(snv(spectrum_scaled), np.array(labels), best_model, n_splits=5 , random_state = random_seed )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9a91ac04",
   "metadata": {},
   "outputs": [],
   "source": [
    "def k_fold_cross_validation2(X, y, model, n_splits=5 , random_state=42 , show=True):\n",
    "    \"\"\"\n",
    "    使用五折交叉验证训练模型，并绘制混淆矩阵。\n",
    "    \n",
    "    参数:\n",
    "    X : numpy array\n",
    "        特征矩阵 (n_samples, n_features)\n",
    "    y : numpy array or list\n",
    "        标签对应的特征矩阵\n",
    "    model : sklearn model\n",
    "        要训练的机器学习模型\n",
    "    n_splits : int\n",
    "        交叉验证的折数，默认为5\n",
    "    \"\"\"\n",
    "    kf = KFold(n_splits=n_splits, shuffle=True, random_state=random_state)\n",
    "    Y_pred = []\n",
    "    Y_test = []\n",
    "\n",
    "    # 进行五折交叉验证\n",
    "    for train_index, test_index in kf.split(X):\n",
    "        X_train, X_test = X[train_index], X[test_index]\n",
    "        y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "        model = SVC(C=1000 , kernel='linear' , gamma='scale')\n",
    "\n",
    "        # 训练模型\n",
    "        model.fit(X_train, y_train)\n",
    "\n",
    "        # 预测\n",
    "        y_pred = model.predict(X_test)\n",
    "\n",
    "        # 汇总结果\n",
    "        Y_pred.extend(y_pred)\n",
    "        Y_test.extend(y_test)\n",
    "\n",
    "    accuracy = accuracy_score(Y_test, Y_pred)\n",
    "\n",
    "    if show:\n",
    "        # 计算混淆矩阵\n",
    "        cm = confusion_matrix(Y_test, Y_pred)\n",
    "\n",
    "        # 获取唯一的标签\n",
    "        unique_labels = np.unique(y)\n",
    "\n",
    "        # 绘制混淆矩阵\n",
    "        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=unique_labels)\n",
    "        disp.plot(cmap=plt.cm.Blues)\n",
    "\n",
    "        # 计算它们的迭代位置\n",
    "        iters = np.reshape([[[i, j] for j in range(cm.shape[1])] for i in range(cm.shape[0])], (cm.size, 2))\n",
    "\n",
    "        # 计算每个单元格的百分比\n",
    "        cm_percentage = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] * 100\n",
    "        # 显示数字和百分比\n",
    "        for i, j in iters:\n",
    "            if (i == j):\n",
    "                plt.text(j, i + 0.12, f'{cm_percentage[i, j]:.0f}%', va='center', ha='left', fontsize=10, color='red')  # 显示百分比\n",
    "            else:\n",
    "                plt.text(j, i + 0.12, f'{cm_percentage[i, j]:.0f}%', va='center', ha='left', fontsize=10, color='red')  # 显示百分比\n",
    "\n",
    "        plt.title(f\"Accuracy: {accuracy:.2f}\")\n",
    "        plt.show()\n",
    "\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eddbda6",
   "metadata": {},
   "source": [
    "### 保存模型并且使用新的模型进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "303e19f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型保存成功\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "import  joblib\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "df = pd.read_csv(rf'E:\\code\\Spectrum_analyse\\SpectralAnalysis\\tobacoo_0830_上午.csv' , index_col = 0 , header=0)\n",
    "labels = df.index.values\n",
    "labels =  [i.split('-')[1] for i in labels]\n",
    "spectrum = df.values\n",
    "\n",
    "scaler = StandardScaler()\n",
    "spectrum_scaled = scaler.fit_transform(spectrum)\n",
    "joblib.dump(scaler, 'standard_scaler.pkl')\n",
    "\n",
    "\n",
    "### 训练并且保存模型\n",
    "model = SVC(C=1 , kernel='linear' , gamma='scale')\n",
    "model.fit(spectrum_scaled, labels)\n",
    "import  joblib\n",
    "joblib.dump(model, 'dayanye.pkl')\n",
    "print('模型保存成功')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "084fafcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 加载模型\n",
    "model = joblib.load('dayanye.pkl')\n",
    "\n",
    "\n",
    "import joblib\n",
    "\n",
    "# 加载之前保存的 StandardScaler 模型\n",
    "scaler = joblib.load('standard_scaler.pkl')\n",
    "\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as pl\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "from SpectrumAnalyse import snv\n",
    "\n",
    "df = pd.read_csv(rf'E:\\code\\Spectrum_analyse\\SpectralAnalysis\\tobacoo_0830_下午.csv' , index_col = 0)\n",
    "labels2 = df.index.values\n",
    "labels2 =  [i.split('-')[1] for i in labels2]\n",
    "spectrum2 = df.values\n",
    "spectrum2 = spectrum2\n",
    "new_spectrum_scaled = scaler.transform(spectrum2)\n",
    "\n",
    "\n",
    "predit = model.predict(new_spectrum_scaled)\n",
    "cm = confusion_matrix(labels2, predit)\n",
    "\n",
    "# 获取唯一的标签\n",
    "unique_labels = np.unique(labels2)\n",
    "\n",
    "# 绘制混淆矩阵\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=unique_labels)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "accuracy = accuracy_score(labels2, predit)\n",
    "plt.title(f\"Accuracy: {accuracy:.2f}\")\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "spe",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
